We train our CNN emulator with 2000 simulations from the Quijote latin hypercube N-body suite [1]}. Each has a unique set of five cosmological parameters: \(\Omega _\mathrm {m}\) , \(\Omega _\mathrm {b}\) , \(\sigma _8\)  \(n_\mathrm {s}\) , and \(h\) . The values of these cosmological parameters are randomly sampled in a five dimensional latin hypercube. Additionally, each simulation has a unique random seed for its initial condition, so all of the initial conditions are different in every simulation. The simulations were run using the N-body code Gadget-3 [2]} with \(512^3\)  particles in a \(1~\mathrm {Gpc}\,h^{-1}\)  box. Our CNNs are trained on the redshift zero simulation snapshots.
